Predicting CVSS Metric via Description Interpretation

Costa, Joana Cabral; Roxo, Tiago; Sequeiros, João Bernardo; Proença, Hugo; Inácio, Pedro R. M.

doi:10.1109/access.2022.3179692

Cited by 12 publications

(3 citation statements)

References 31 publications

(35 reference statements)

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“…In contrast, lemmatization goes further, transmuting words into their canonical manifestations (e.g., "better" into "good"). These techniques engender word standardization, diminishing lexical variations and potentially heightening the precision of text analysis [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

EU space security–An 8-Step online discourse analysis to decode hybrid threats

Reis

2024

PLoS ONE

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Space security has emerged as a concern for the European Union (EU), given that space systems have become integral to ensuring the safety of all European society. This strategy reflects the interaction of geopolitical dynamics and the rising specter of hybrid threats. However, grappling with hybrid threats targeting the EU space presents distinct challenges, primarily owing to their elusive nature. Hence, our objective is to develop practical methodologies to identify and mitigate such threats effectively. To meet this objective, we propose an innovative 8-step process. This approach streamlines the systematic identification and analysis of discourses of hybrid threats within online platforms dedicated to EU space discussions. Our methodology underwent rigorous scrutiny, including a comprehensive literature review that prioritized peer-reviewed manuscripts from sources such as Web of Science and Elsevier Scopus. Additionally, we selected documents from Google Scholar to ensure comprehensive coverage of diverse scholarly contributions, enriching the depth of our analysis. Our research yielded a conceptual framework for Online Discourse Analysis (ODA) tailored to evaluate hybrid threats targeting EU space defense and security. The results highlight the importance of leveraging advanced ODA techniques to deepen our understanding of emerging threats. In conclusion, we advocate for the adoption of these innovative methods to enhance the robustness of EU space defense strategies in the face of evolving security scenarios. The establishment of the 8-step ODA framework marks a pivotal milestone, offering a structured approach to deciphering hybrid threats. Looking ahead, we aim to empirically validate this framework by creating and deploying custom-tailored software designed to identify potential hybrid threats jeopardizing the security of European space assets. Through continued research and practical implementation, we endeavor to fortify the EU’s defenses against emerging threats in the space domain.

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Section: Methodsmentioning

confidence: 99%

EU space security–An 8-Step online discourse analysis to decode hybrid threats

Reis

2024

PLoS ONE

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“…Tese methods bring convenience to CVSS assessment work. Shahid and Debar [37], Gong et al [38], and Costa et al [39] applied pretrained models and deep learning algorithms to vulnerability metric prediction work. However, these methods do not give the fnal vulnerability severity level prediction.…”

Section: Related Workmentioning

confidence: 99%

VulDistilBERT: A CPS Vulnerability Severity Prediction Method Based on Distillation Model

Kai

Shi

Zheng

2023

Security and Communication Networks

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With the vigorous development of the Internet, the ecosystem of cyber-physical systems is also developing at a high speed, but cyber-physical systems may be accompanied by unknown vulnerabilities in the process of concrete implementation. Thus, the number of vulnerabilities in cyber-physical systems has been increasing year by year. The vulnerability evaluation speed cannot keep up with the vulnerability exposure speed. The traditional manual evaluation method can no longer effectively deal with such large-scale vulnerabilities, resulting in a backlog of vulnerabilities. Therefore, the vulnerability evaluation results have a certain lag. To address this problem, the paper proposes a vulnerability severity assessment method based on the distillation model. The method first uses data augmentation and integration of optimal subsets to improve the amount of information in the vulnerability description text, then uses the DistilBERT model to characterize the text of the vulnerability description text, and then the characterized feature vectors are classified based on the linear layer to achieve the purpose of assessing vulnerability severity. Compared with the current method of manual assessment based on the CVSS metric system, this method can automate the assessment of vulnerabilities based on vulnerability description text, which improves the speed of vulnerability assessment, and the assessment accuracy and other metrics achieved by this method are improved compared with similar studies. This approach provides an automated solution for cyber-physical systems vulnerability assessment and can better address the current situation where cyber-physical systems vulnerabilities are being exposed at an accelerated rate.

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“…Gong, X. [24] and Costa, J.C. [25] improved the predicted measurement by applying the method of combining pre-training model and deep learning to the prediction of CVSS measurement. Spanos, G. [26], Ali, M.…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent recognition method of software vulnerability severity based on event extraction

Wen

Lin

et al. 2023

Preprint

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The number of vulnerability reports is rapidly increasing, and relying on human resources for vulnerability analysis will become more difficult. In addition, the current vulnerability report research is mainly based on general vulnerability disclosure (CVE) or other foreign open source projects, and there are few researches on Chinese vulnerability reports. With the development of deep learning, the research on event extraction has also been greatly developed. In order to contribute to the vulnerability analysis of Chinese vulnerability report, this paper adopts the method of event extraction to automatically mine the vulnerability report of the National Information security Vulnerability Sharing platform (CNVD). A new vulnerability analysis method is proposed, that is, the severity level of vulnerability report is evaluated by the vulnerability description information and the event information. At the same time, a convolutional neural network method named EICNN is proposed to realize this new vulnerability analysis method by integrating event information. An application that obtains the vulnerability reporting event information and evaluates the vulnerability severity at the end.

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Predicting CVSS Metric via Description Interpretation

Cited by 12 publications

References 31 publications

EU space security–An 8-Step online discourse analysis to decode hybrid threats

EU space security–An 8-Step online discourse analysis to decode hybrid threats

VulDistilBERT: A CPS Vulnerability Severity Prediction Method Based on Distillation Model

Research on intelligent recognition method of software vulnerability severity based on event extraction

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